Phone-Level Embeddings for Unit Selection Speech Synthesis

Abstract

Abstract. Deep neural networks have become the state of the art in speech synthesis. They have been used to directly predict signal parameters or provide unsupervised speech segment descriptions through embeddings. In this paper, we present four models with two of them enabling us to extract phone-level embeddings for unit selection speech synthesis. Three of the models rely on a feed-forward DNN, the last one on an LSTM. The resulting embeddings enable replacing usual expert-based target costs by an euclidean distance in the embedding space. This work is conducted on a French corpus of an 11 hours audiobook. Perceptual tests show the produced speech is preferred over a unit selection method where the target cost is defined by an expert. They also show that the embeddings are general enough to be used for different speech styles without quality loss. Furthermore, objective measures and a perceptual test on statistical parametric speech synthesis show that our models perform comparably to state-of-the-art models for parametric signal generation, in spite of necessary simplifications, namely late time integration and information compression.

Corpus design for expressive speech: impact of the utterance length

“…More recently, most unit selection systems shifted to an hybrid architecture that includes DNN to learn the cost functions [16]. Following this trend, the second system for the experiments, called hybrid TTS, is inspired by [17]. Its target cost is computed based on an euclidean distance in an embedding space.…”

Corpus Design Using Convolutional Auto-Encoder Embeddings for Audio-Book Synthesis

“…We define a linguistic feature vector for each phoneme in the utterance, extracted from a text utterance, and providing information about the phoneme, e.g., its identity, preceding and following neighbours, its position in the syllable/word/utterance it belongs to, etc. The linguistic features are automatically extracted [17]. The linguistic vector, of size 296, contains categorical and numerical features.…”